{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Discover and Transformation\n",
    "in this section of the lab, we'll use Glue to discover new transportation data.  From there, we'll use Athena to query and start looking into the dataset to understand the data we are dealing with.\n",
    "\n",
    "We've also setup a set of ETLs using Glue to create the fields into a canonical form, since all the fields call names different things.  \n",
    "\n",
    "After understanding the data, and cleaning it a little, we'll go into another notebook to perform feature engineering and time series modeling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What are Databases and Tables in Glue:\n",
    "When you define a table in the AWS Glue Data Catalog, you add it to a database. A database is used to organize tables in AWS Glue. You can organize your tables using a crawler or using the AWS Glue console. A table can be in only one database at a time.\n",
    "\n",
    "Your database can contain tables that define data from many different data stores.\n",
    "\n",
    "A table in the AWS Glue Data Catalog is the metadata definition that represents the data in a data store. You create tables when you run a crawler, or you can create a table manually in the AWS Glue console. The Tables list in the AWS Glue console displays values of your table's metadata. You use table definitions to specify sources and targets when you create ETL (extract, transform, and load) jobs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "\n",
    "database_name = '2019reinventWorkshop'\n",
    "\n",
    "## lets first create a namespace for the tables:\n",
    "glue_client = boto3.client('glue')\n",
    "create_database_resp = glue_client.create_database(\n",
    "    DatabaseInput={\n",
    "        'Name': database_name,\n",
    "        'Description': 'This database will contain the tables discovered through both crawling and the ETL processes'\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will create a new database, or namespace, that can hold the collection of tables\n",
    "\n",
    "https://console.aws.amazon.com/glue/home?region=us-east-1#catalog:tab=databases\n",
    "\n",
    "![create db response](images/createdatabaseresponse.png \"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use a crawler to populate the AWS Glue Data Catalog with tables. This is the primary method used by most AWS Glue users. A crawler can crawl multiple data stores in a single run. Upon completion, the crawler creates or updates one or more tables in your Data Catalog. Extract, transform, and load (ETL) jobs that you define in AWS Glue use these Data Catalog tables as sources and targets. The ETL job reads from and writes to the data stores that are specified in the source and target Data Catalog tables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "crawler_name = '2019reinventworkshopcrawler'\n",
    "create_crawler_resp = glue_client.create_crawler(\n",
    "    Name=crawler_name,\n",
    "    Role='GlueRole',\n",
    "    DatabaseName=database_name,\n",
    "    Description='Crawler to discover the base tables for the workshop',\n",
    "    Targets={\n",
    "        'S3Targets': [\n",
    "            {\n",
    "                'Path': 's3://serverless-analytics/reinvent-2019/taxi_data/',\n",
    "            },\n",
    "        ]\n",
    "    }\n",
    ")\n",
    "response = glue_client.start_crawler(\n",
    "    Name=crawler_name\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After starting the crawler, you can go to the glue console if you'd like to see it running.\n",
    "\n",
    "https://console.aws.amazon.com/glue/home?region=us-east-1#catalog:tab=crawlers\n",
    "![startcrawlerui](images/startcrawlerui.png \"\")\n",
    "\n",
    "After it finishes crawling, you can see the datasets (represeted as \"tables\") it automatically discovered.\n",
    "![crawler_discovered](images/crawler_discovered.png \"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Waiting for the Crawler to finish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING\n",
      "RUNNING\n",
      "STOPPING\n",
      "STOPPING\n",
      "finished running READY\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    " \n",
    "response = glue_client.get_crawler(\n",
    "    Name=crawler_name\n",
    ")\n",
    "while (response['Crawler']['State'] == 'RUNNING') | (response['Crawler']['State'] == 'STOPPING'):\n",
    "    print(response['Crawler']['State'])\n",
    "    # Wait for 40 seconds\n",
    "    time.sleep(40)\n",
    "    \n",
    "    response = glue_client.get_crawler(\n",
    "        Name=crawler_name\n",
    "    )\n",
    "\n",
    "print('finished running', response['Crawler']['State'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying the data\n",
    "\n",
    "We'll use Athena to query the data.  Athena allows us to perform SQL queries against datasets on S3, without having to transform them, load them into a traditional sql datastore, and allows rapid ad-hoc investigation.  \n",
    "\n",
    "Later we'll use Spark to do ETL and feature engineering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --upgrade pip > /dev/null\n",
    "!pip install PyAthena > /dev/null"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Athena uses S3 to store results to allow different types of clients to read it and so you can go back and see the results of previous queries.  We can set that up next:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "sagemaker_session = sagemaker.Session()\n",
    "athena_data_bucket = sagemaker_session.default_bucket()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we'll create an Athena connection we can use, much like a standard JDBC/ODBC connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     type  ride_count\n",
      "0   green    12105351\n",
      "1  yellow   147263398\n",
      "2     fhv   292722358\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa3420bbeb8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyathena import connect\n",
    "import pandas as pd\n",
    "\n",
    "sagemaker_session = sagemaker.Session()\n",
    "\n",
    "conn = connect(s3_staging_dir=\"s3://\" + athena_data_bucket,\n",
    "               region_name=sagemaker_session.boto_region_name)\n",
    "\n",
    "df = pd.read_sql('SELECT \\'yellow\\' type, count(*) ride_count FROM \"' + database_name + '\".\"yellow\" ' + \n",
    "                 'UNION ALL SELECT \\'green\\' type, count(*) ride_count FROM \"' + database_name + '\".\"green\"' +\n",
    "                 'UNION ALL SELECT \\'fhv\\' type, count(*) ride_count FROM \"' + database_name + '\".\"fhv\"', conn)\n",
    "print(df)\n",
    "df.plot.bar(x='type', y='ride_count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "response from starting green\n",
      "{'JobRunId': 'jr_466ee6fbc9356bdaf875f815035e823c382666cc060e38092fe91d5411ae0546', 'ResponseMetadata': {'RequestId': 'bf2b5ed1-2b2b-11ea-9cf9-c754cb1c941b', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Mon, 30 Dec 2019 17:42:28 GMT', 'content-type': 'application/x-amz-json-1.1', 'content-length': '82', 'connection': 'keep-alive', 'x-amzn-requestid': 'bf2b5ed1-2b2b-11ea-9cf9-c754cb1c941b'}, 'RetryAttempts': 0}}\n"
     ]
    }
   ],
   "source": [
    "green_etl = '2019reinvent_green'\n",
    "\n",
    "response = glue_client.start_job_run(\n",
    "    JobName=green_etl,\n",
    "    WorkerType='Standard', # other options include: 'G.1X'|'G.2X',\n",
    "    NumberOfWorkers=5\n",
    ")\n",
    "print('response from starting green')\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After kicking it off, you can see it running in the console too:\n",
    "https://console.aws.amazon.com/glue/home?region=us-east-1#etl:tab=jobs\n",
    "<img src=\"images/ETLStart.png\"/>\n",
    "\n",
    "<b>WAIT UNTIL THE ETL JOB FINISHES BEFORE CONTINUING!</b>\n",
    "<b>ALSO, YOU MUST CHANGE THE BUCKET PATH IN THIS CELL - FIND THE BUCKET IN S3 THAT CONTAINS '2019reinventetlbucket' in the name</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-12-30 17:08:43 aim357-template2-2019reinventetlbucket-144yyhe1x8qgo\n"
     ]
    }
   ],
   "source": [
    "#let's list the s3 bucket name:\n",
    "!aws s3 ls | grep '2019reinventetlbucket' | head -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# syntax should be s3://...\n",
    "normalized_bucket = 's3://aim357-template2-2019reinventetlbucket-144yyhe1x8qgo'\n",
    "\n",
    "\n",
    "\n",
    "## DO NOT MODIFY THESE LINES, they are there to ensure the line above is updated correctly\n",
    "assert(normalized_bucket != 's3://FILL_IN_BUCKET_NAME')\n",
    "assert(normalized_bucket.startswith( 's3://' ))\n",
    "\n",
    "create_crawler_resp = glue_client.create_crawler(\n",
    "    Name=crawler_name + '_normalized',\n",
    "    Role='GlueRole',\n",
    "    DatabaseName=database_name,\n",
    "    Description='Crawler to discover the base tables for the workshop',\n",
    "    Targets={\n",
    "        'S3Targets': [\n",
    "            {\n",
    "                'Path': normalized_bucket + \"/canonical/\",\n",
    "            },\n",
    "        ]\n",
    "    }\n",
    ")\n",
    "response = glue_client.start_crawler(\n",
    "    Name=crawler_name + '_normalized'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's wait for the next crawler to finish, this will discover the normalized dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING\n",
      "STOPPING\n",
      "STOPPING\n",
      "finished running READY\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    " \n",
    "response = glue_client.get_crawler(\n",
    "    Name=crawler_name + '_normalized'\n",
    ")\n",
    "while (response['Crawler']['State'] == 'RUNNING') | (response['Crawler']['State'] == 'STOPPING'):\n",
    "    print(response['Crawler']['State'])\n",
    "    # Wait for 40 seconds\n",
    "    time.sleep(40)\n",
    "    \n",
    "    response = glue_client.get_crawler(\n",
    "        Name=crawler_name + '_normalized'\n",
    "    )\n",
    "\n",
    "print('finished running', response['Crawler']['State'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying the Normalized Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at the total counts for the aggregated information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    type  ride_count\n",
      "0  green    12105351\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa341e78c18>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "normalized_df = pd.read_sql('SELECT type, count(*) ride_count FROM \"' + database_name + '\".\"canonical\" group by type', conn)\n",
    "print(normalized_df)\n",
    "normalized_df.plot.bar(x='type', y='ride_count')\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa3422d5278>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query = \"select type, date_trunc('day', pickup_datetime) date, count(*) cnt from \\\"\" + database_name + \"\\\".canonical where pickup_datetime < timestamp '2099-12-31' group by type, date_trunc(\\'day\\', pickup_datetime) \"\n",
    "typeperday_df = pd.read_sql(query, conn)\n",
    "typeperday_df.plot(x='date', y='cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We see some bad data here...\n",
    "We are expecting only 2018 and 2019 datasets here, but can see there are records far into the future and in the past.  This represents bad data that we want to eliminate before we build our model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setting index to date\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-18</th>\n",
       "      <td>green</td>\n",
       "      <td>25602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>green</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-07</th>\n",
       "      <td>green</td>\n",
       "      <td>25159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-28</th>\n",
       "      <td>green</td>\n",
       "      <td>24517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-21</th>\n",
       "      <td>green</td>\n",
       "      <td>25620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             type    cnt\n",
       "date                    \n",
       "2018-03-18  green  25602\n",
       "2019-07-01  green     15\n",
       "2018-06-07  green  25159\n",
       "2018-06-28  green  24517\n",
       "2018-06-21  green  25620"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Only reason we put this conditional here is so you can execute the cell multiple times\n",
    "# if you don't check, it won't find the 'date' column again and makes interacting w/ the notebook more seemless\n",
    "if type(typeperday_df.index) != pd.core.indexes.datetimes.DatetimeIndex:\n",
    "    print('setting index to date')\n",
    "    typeperday_df = typeperday_df.set_index('date', drop=True)\n",
    "    \n",
    "typeperday_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa3422680b8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "typeperday_df.loc['2018-01-01':'2019-12-31'].plot(y='cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at some of the bad data now:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the bad data, at least the bad data in the future, is coming from the yellow taxi license type.\n",
    "\n",
    "### Note, we are querying the transformed data.\n",
    "\n",
    "We should check the raw dataset to see if it's also bad or something happened in the ETL process\n",
    "\n",
    "Let's find the two 2088 records to make sure they are in the source data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendorid</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>ratecodeid</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>pulocationid</th>\n",
       "      <th>dolocationid</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2088-01-24 00:15:42</td>\n",
       "      <td>2088-01-24 00:19:46</td>\n",
       "      <td>1</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>41</td>\n",
       "      <td>166</td>\n",
       "      <td>2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2088-01-24 00:25:39</td>\n",
       "      <td>2088-01-24 07:28:25</td>\n",
       "      <td>1</td>\n",
       "      <td>4.05</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>24</td>\n",
       "      <td>162</td>\n",
       "      <td>2</td>\n",
       "      <td>14.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>15.3</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vendorid tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "0         2  2088-01-24 00:15:42   2088-01-24 00:19:46                1   \n",
       "1         2  2088-01-24 00:25:39   2088-01-24 07:28:25                1   \n",
       "\n",
       "   trip_distance  ratecodeid store_and_fwd_flag  pulocationid  dolocationid  \\\n",
       "0           0.63           1                  N            41           166   \n",
       "1           4.05           1                  N            24           162   \n",
       "\n",
       "   payment_type  fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
       "0             2          4.5    0.0      0.5         0.0           0.0   \n",
       "1             2         14.5    0.0      0.5         0.0           0.0   \n",
       "\n",
       "   improvement_surcharge  total_amount congestion_surcharge  \n",
       "0                    0.3           5.3                 None  \n",
       "1                    0.3          15.3                 None  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(\"select * from \\\"\" + database_name + \"\\\".yellow where tpep_pickup_datetime like '2088%'\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa33e065828>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j9MC17XBYdiXupsqUBABMrInLa/PXWpCiKcU3dP7ePGU3FRcDhggRYnhxzTXXYN26dcjlcrjqqqtw4oknjvQlDRqDym7inHcxxlYBeAuAesZYRKqJFgC75G67AEwH0MoYiwCogwhg03aCfky57YNCMH4QdB0BnoG1A24feh1UDjs7sto5xL6ZvN9Y6wVxtsOVGwqQgetCsbvJUxLlgxJEEhOqBElQMZ0TUDSlBhvRNahiukBRX4gQIYYf+oCisYoBYxKMsYlSQYAxlgRwDoD1AFYBeI/c7SoAD8ifH5SvId//KxcO8AcBvI8xFpeZUfMBPA/gBQDzGWOzGWMxiOD2gwdzM/oK3nLcgJJwwbkXLyhyP1GjP60bbEemgA/8/DntHMKXT8aZDD21vgCEu0m/DtflakhQTSLikZJUFP1NrjvQ56/7CGY3KbIqUYFN9xNMgd3RninaN0SI8YgjPe42VPdfiZKYAuCXMi5hALiXc/5Hxtg6AL9hjN0I4GUAP5f7/xzA/zHGNgPogDD64Jy/zhi7F8A6ADaAT0g3Fhhj1wJ4FCIF9g7O+esHczOFQBGb3v7bcTksh4OeWyVKojfnTytzXY687Sr3jk4sVTET6YID2+G+oUC2y9XqvT4VVQbdC5iXdztRcaBXDU4EJxVNoF7DV8QXyG6ieEroagpxJCCRSKC9vR2NjY2qG8GRBM452tvbi9JqDwYDkgTnfC2AE0ps3wqRmRTcngNwWZlz3QTgphLbHwLwUAXX2y/0mITtuMhprhXL4T5XS7lAtq4+giNN7UB8gbKEMpaDybUJpAsOLMdFQa+TkNlNBgOq41HP3eUE3U7F6EgHSSIYk7DVvXHud6952U1+1VNuBkaIEOMJLS0taG1txf79R27340QigZaWlkM+z7iquNZTYAuOq+oKAGGs83bxStsKuJv0wHWm4FcSjsuViwcgleCCc+FK2tMttunqwOWCWKpiEURN5mU3BRRMKVC7D1JIweymdEAp+dxrkji8thz+nlExc9w1AA4RQiEajWL27NkjfRnjAuPKUhQC7pZgTCLvyzoqbaz11XgmoCQclyNjCeKImgy24yq1UZuIqvMFezel8zaq4hFEDKbVZ/Sf5ZS3HXW9RGjBoUPZgp5ay311IRSTCWZ1EUlETE+C5/rpVxUiRIgjG+OLJAJKIZjdlA+ks7ou9+ILARcOUNrdREqjOh6BrfVlqkkIUWY53NeWw3U50nkH1YkIIqahBa7F/y4v3TKjvc9zCwWn56nWHnnHt08w5Zcym+j+AS+QTecs2C5O/OpjuPz2Z4/4QF+IECGKMW5JwnLdohqHfKB+QQ0EYp7h1WMOpdxNdM6qeES4eEhJJKWScN1AxbU4JymJYF0GUHog0H6to20hEJMgYgm6l/yvXUUIdF0A0J314hiuy9Gbs5ApOPjH1vaibraEbMHBhr1D22o5RIgQYwPjlyQ0VxAgOrj6YhJa+4xExFSGty9nK1WQLqkkxLbqeASW45GEriR8BOC6SOdtVMdNREzDy6rS1EOpFFadJCy7RMuQQONAy/Xfr+VwX2ouJVzp1dmW6/pcauVahdz88Hqcf+vfsbsrW/L9ECFCjF+ML5LoNyYRqJ7WVtoTqmJqn968jfqUUAWkJP70ydNgGgyu5m4SSsJz8egxCV9Rnxa4jhhe4NrxpckWG2e9RiLobhLHc2QKDqrjEXW/uUD2FrmjElFDUxIeSRRsv4sq2CCRQOTw1OYDJd8PESLE+MX4IomAkvC32HCLlERnWhjMJtn2wnZd9OVt1CcFadAqOx4xYRrMV/NQFY/AdrgikhoiCddTEiJQLdxN1QF3k28OdgklQTUaqZjpkURg1Z8p2KgjN5fjrwuxNAKrjkdBH9GrKwlNCZW7DgCYLmd5P7e1o+T7leI/7nsFH/rF84d0jhAhQhxejGmSSOdtnwukECCB4Mo7H3jdlRXB4YnVghR6sjY4hzK8WUUSBkzG4Lgu8pJ4amTgmtQJDfkpyOwmg4mZDo6MjSRjJqKau8kOZEAFQbGRumRUi0m4iEcMee8OXK65uVy3OEahucIcTUkYzHtelbibKO1Xd2898tpefOmB10ruT+fW3V0AcO/qVqzaeOTmrYcIMRYxpkni5ofX4623/BWv7+4GAOQdz4hadiBw7XiFcPGIMNYUqG2qFkqCSKNOuZs8kohIJZFTSsJULh/AUxIf+sUL+MGqzYiaBkypJPKW66mREvURpdxNmYKNZNREImrCdjgcV8y2IFIgV5muJIKB66wWP6G6ie6spRoGCrXlj2OUQloV7XnX+bG7XsSd/9hecn8A+NDK57Ho+j9r5w7Hp4YIMRYxpkmCWnQ/9OoeAGL1WhWnlbUI7NbI147L0dopVMeMCSk4LkennMNAJEHuJzK85EqKR0yYJsUkPHeTHvitTfjrEokkXC4C5vGogYjJlCEu1UJDR7rgoCpuImoynzEnMuoNkIQeqI9FDFha5lV1PAKXi5RY2+WYXCtK9fMBJVGKrMRzEPuQoqkkVfbpze2+fcN2ICFCjE2MaZJorKIVsTBEBdtRM6NpJe2lpnJsb89gcm1CxBN8SkK4m7qlkqiXx1DgNx7VlIRWJ8E5imIShIjJYBpMtulwETMNRA1RJ3HPCztKDjPSkc7bSMUiiMraCl0VAF531zrt/ijmUhOP+JVEQtxvhyTFSZIkhLtJb2NemiTIzUTuPCJbYGDCoGsKGwuOXqTzNr7+yIawqDJESYzpthzUQZVW4gXHRVXMS0VN5710Vsd1sbMjgxkTUnA5h+O66MoUkIyaSn10StJQ2U3yjyZmGjAYU3USEYMhIWdTU4C5Lul/lBGDAWDKUMejBkyToa03j/+8/1Xfvk6JFXw676AqLkii4HjpruXdTUJJmAZDMmaqYkKDiTnarstV36bJdQn5jEq7m1Zv6wBjwNKZE8S1SCIhJeHPvOKIRco3UOvJWUhETfUsQ4w+/PTvW/HDJ7agsTqOD58WtrII4ceYVhK0hiU3iXA3CeMtsn8clZpqORzbO9KY0ZiSsQGOzoyF+lRUtaggZeEFrm1ETQbDYKqlBgWPTRn9bU8XkIqZiEdM37UZTByT1TKkokZpY1pKSWQKNqpi0t1ku6oj7JS6JACvuysppYLjojNTQF0yKtSHjJcko6ZSQUQSpCRyloPfr9mtPpOUxHt+/A9c+qN/eNeSd3zv6wWH5VxUBJrKlyuEJDFaQRX/enp0iBCEMUsSImjs74KaKThKFdgyXkBZR45MeW2qjiNiCoO/tzuH5po4IoZ4DO1yhdyQ8lJgyfib8pic7SAhDS8dQ4ZZR8QQ7qaMFicwDf8+xBklYxJ5G6m4527a1yPiL9PqhYHvzBQHrvf15OX9MOVuSsZMGUDn6CQlIUnizme348XtneozbVmFTQiOayV3kz6OlQr9yoGUVjZUEqMGNz+8HnO+8Cf1Oi5VcTiQKkQpjFmSWLenBw+9uheAcLXs68mjN2fj6Ek1AARx6EoibzsoOC5SMRMRQ6y03zyQxqymKmXwd3RkwBgwtV6s1gVJiEdkMlknYbk+kuhIi9W73jAPAAyDwTD8abTRwD7ksioZkyg4qI6bGkkIApvWIK7t24+9AUCPSbho682huTYhe0SJuolkzFSkSDEJcjfps7kBoUb29HjbaCyrpyTkkCVNSZQrwCOEJDH68JO/bYXLPZclfcfzVpiBFqIYY5YkdNguV2mwx0+vF9tkoRu5Y8iwkfslk7exuzuLWY1ViEgVsKMjg0k1CSSiVItgeyRheEpCxBdIfRRQm4wiGlAJpsEQMQzVNTYeMYqIRJFECUOb8QWuOdp6cohFDDXKlEBKSeyTx6SauGpJnrWEu4niKR3pAuIRwxfs9j1Hh2NLW596vbmtzzeJr5SSKOVu0oPZlIVFisos43ILUQzOOa5/8HW82to9LOffJjPOVCv60CUYogTGBUkc6Mvjw79cDQA4vkWQRMF2kLUcFeilFS25X948kAbnwGxNSezsyGBqfUK5hUTqqjDkEcNQBXkJLb7QXkZJmAaDwfy1FkF3U3IAJVEVMxGLMOVumlQbL/ocyvAq2C729+UxqTahKruJJCIGg8MF0TTXxhGTxLcn0IvJclzs7PSykA70FZCzvEl8hRIxiVLuJj2tlp47BchpUFOIgdGRLmDlM9twxU+fHdLzUro2pSUTOaQDDS1DhADGCUk896ZoF3HZ0ha1Sk4XHHAu6hkM5m9zETGZMswtDUlleNMFB1PrkzC1cYc0nIfacuQsF4moF7juzlqoT0aV4iCYTMQk+gtcx6ViCcYkOPdmUOjupkk1Cd/nfOmfFqJa/sHv68nBcTmaa+OqJXmmIOInhsHgOBx7unOYXJtQ8ZO2Xv8MbctxcaDXa1FuO64iBErnBQIk4Xq1E89sOQDOuW/sK7k09FVqP3OWxjV+9vetWLWhTb3W55EAwGu7unHCV/6sssco7qS794YC9TLmtu2AWBBQqne5LsAhjmyMC5IgN8iX3rlQGXzK/qEYRJ80Vsmo6VvR1yajKnANiD5FprZab5Q1FMLdJFJGE1HTt6IXSqLY3WQahj9wHVABnpLwu2z68qLorSYhrs1yOPb15jCpNqFcVAAwtT6h3Fy7pCporkloNR2OvH/xWqiRhFISAHB8Sx3++u9nyOvg2N/nxSQsl6uMl6bqmHrOOgkQcdz3Yiuu+OlzeGDNbl87jlIxifFafX3fi62+RIAgbvzTenxo5QsqFvSZe1/Bgi89ohIEfvTEFnRmLDwtGymWGzX7+u7uQT3DVRva8Nouz2VFqo5+t1npEm0PR9uGKIFxQRIAMKsxhZpEVK3waQWbikVgGsznbopoK/rqeMRn8D96+hyfkqBMINNgcLgwdiJw7T06Ui86DMZgGp5xjEcMJAJpsmTwH1u3D7M+/ye0yaDx+j29AIBjJtcgFmEoOC7aevJoro0rYhHn9MhqlyxwIyVBq1ThXhOusr09QklUxXSiSaq4DSkJatuhd8ptqo6XVhLS3bS7S1z75rY+n8uK6jt8bdvHgZTYsr8P29u9KnLX5fjsb1/BpT96puT+etYYEfqDr4j041dauwB45EnqlWac63h9dzcu+t5TuG3V5oqv9UMrX8A/ff8pdX4iB1qc0EKGJh0+s/kAfvK3LRWfP8T4xoAkwRibzhhbxRhbxxh7nTH2Kbn9esbYLsbYGvnvQu2YLzDGNjPGNjLGztO2ny+3bWaMfV7bPpsx9pzcfg9jLDbYG6GsHyIAqkimlXSQNAhV8Yj6I1k6swETqmK+9yfJTKCIVBLZglidpzRDS72edERMBpMxtfqOR0xVpEcgg3/3czsAAK3SeNCq79iptYiZBjrTBfTl7SIlITKmxK+QMpEm1SZQHTexoyODbe1ptDSkYBoinpCzXEyuS6g0YQBoqIqpc1gOx4G+vCJG29GVRFwNKurL+WdSAFDB/pzl4MXtnTANhlTMK6ILNh+sFOff+mS/jQRHChff9jTO+MYT6JIZYzs6iivKO7WVue4yokSFOU1VAKCUAxnxiCIJq+gY+m7on/e/D63HPS/sGPCaX97RhZzljcWlbDX6/tPrK372HG5+eEPZ0bohjixUoiRsAP/OOV8I4FQAn2CMLZTvfYdzvkT+ewgA5HvvA3AsgPMB/JAxZjLGTAC3AbgAwEIAl2vn+Zo81zwAnQA+PNgboVV6UElQCmifFpPQU1Gr4xEsmV6P0+Y14RvvWQwAPqVBBtOQweCMJIm5E6vVPrMaq4qux2BMpZwCwt1E9RfqmqP+xx8zDZx04+P4yh/XobEqhubaBJqq4yp+MimoJKLevZCSmFgdx5Lp9ejMWLAcjosWTfG51ybJmAS5nGoSEXWOgu1iV1fWq8h2XeW2o/5Wluv63U2KBL1g/0vbu7BwSi0mVMWUgvC1JB+gAI+wuyuLDXt7+20kOBzIWQ6e2dL/7Ax6BtSjav0eMblvmkyffn13N0746mN4YM0uAJ77E9DayNAY2sDscjLOupLoksdTS5QW+TkAcPuTW4uq+Eshbzu+gjk7kNUUTGfe2Tm8Q6YyBRtf+N2rYRHfKMeAJME538M5f0n+3AtgPYBp/RyyAsBvOOd5zvmbADYDWCYy5BcoAAAgAElEQVT/beacb+WcFwD8BsAKxhgDcCaA++TxvwRw8WBvhIwek5XO5LKZ3pCCaRjqjzoRNRWRpGSmU10yirs+cgrmSMNv6EqiVlcSIq02FYuoGQsAsGBKrdqHYBoMOzu8P7J4xChSEvWpYsFEQUtSJ1M1Y9CspefSOWnV2ZmxMKEqhljEwMmzJqh9FrfU+a6LYizk0qhNeIWA335sI/Z05zClzlMSiiRqxHEF28We7pwyhmTwSOHkLAc7OzOY11yNVMxUBkhvy1EqmwsQxH761/+K52UiwpNviLbiRNSHCz/7+1Zc8dPn8NSmgYcsFRxxX5tk6vBsqQ427ROvf/+yJAldfTkuOOfqd01qk/4n1aXHCEh9EUnoLsJ+ry8w910PTgfnutO5yNu6WUuHHg78+vmd+PXzO/DDQbjOQhx+DComwRibBeAEAM/JTdcyxtYyxu5gjDXIbdMA7NQOa5Xbym1vBNDFObcD20t9/jWMsdWMsdXB9/SsH9NgONCXx4SqGOZOFCmuJPcpkA3A53bREfGRRFyd05FV3ZRGSyAfvl51HawHiEeLlYROAACgZ4ZSQJrcaIAogtPdTXpRHwA0y+tYNK0OV71lJh7+1OlgMsuKQCmzlIZak4ioc5DB/+CpMwFQTEI8tyZ5XKbgYE93FnMmCmNoua5Iv5WZUjnbRXtfAY1VMSS1nk2+gUhlDNsTG/djZ0dW+cMpu4d+B5xz/PCJzb7eUa7rrxIfCvTJbJ8nNnqZSD/+2xac9a0nilww9MzomuhZU4fh/XI7uT8BsWLPFBzV5JEMORltIgndoNMzo3gGfe7+3uK4hY60r4UK94+vdfwxCVKF9B0ZbpKgb6U+DCzE6EPFJMEYqwZwP4BPc857APwIwFwASwDsAfCtYblCDZzz2znnJ3HOTwq+p2fskNE7YXp9kZFMaQa+pgxJ6PvTap/6MNkuR0oa6q9dugj/c9ECta/uxjIZU+4rQKzcgyQRCRAJh2fsorJp3jSNSGY3VvnIUI9JAEAzqR7TwA0rjlMKR7+fhiqxAiW7WpOIgDGmrv24abWYP6kGEYOp7KZE1FCptm8eSMPlUO42y3Zx7a9ewrdkBXhnuoCs5aCpJo5kzFTB0OAAqFLQYzGAZ+Do+jfu68XXH9mI6371sjpm8Q1/xhU/G9o6Anqk6/cKF1K24OCWhzdgy/40MpbjIyVa4dOqn1xpe2QGU5uslNeVhO1wH9Epkgi4f3QXFREIbSMDv7fHXzUfRF8gFtKVKVYnwVbw1HCyva9/AjpU0HeuUvdjiJFBRSTBGItCEMTdnPPfAQDnfB/n3OGcuwB+CuFOAoBdAKZrh7fIbeW2twOoZ4xFAtsHBb3BHhkVZeA1462vvqsTA5MEZS4FM6QA4L0nz8BHTp+j9o0F1MxlJ3m3G4+aqrCPECw+9ikJaakmaa4Ww2BgzH8vVLQHAJNq/NXY6lq0Y4ioqINuTVzcH6kr9Vq28ujJWj6X1Jb9YnU5VyoJ2+X487p96vy00m2qjiMV85IC9Nke5YYbbdgrXIQJ+XzJwJGBpLYRr+320jn78jaePcSxqkHQCp5W+vpqPW+5AdeZjB/0CeNL2V70HEoZfMtx/STh+APJlKKqx36IQGgbvd4nyai6zILHPy/EUxLV8Yi6diIwSw63on2GurhuX08Of3vDm0xIsbLxkO02nlFJdhMD8HMA6znn39a2T9F2uwQApaA8COB9jLE4Y2w2gPkAngfwAoD5MpMpBhHcfpALv8cqAO+Rx18F4IHB3ohuoMmg0UqFjL7B4OvgWu4PSzeqZNhNgymjVc5Npa/qjQADxEyjaBtjDItb6tRr/U+FzhWLGLjpkuPw8KdOL/o8KsajoPKkMr57nfTovFxTEoBHpPSaZl90Zy3UJqPq+W5pE2mfFL8Juo4ogN5YHZNKwkHOctCeLijXWTmjQKtcMrTBORZkIINV3DpuW7UZx37pkZLnrxTdgdW67qIpOK6/waE07FTTQNleNFaXXCnBc+ztLlYS2UAmWG/AVQRoz0i+psaPpBCD0JWETgBN1THR38ty0JWxVKyrM1NQKpPcbkOF9/z4GVx1x/PK1Rl0cwLAR+9cjd+u3lny+BAjg0qUxHIAHwRwZiDd9euMsVcZY2sBvAPAvwEA5/x1APcCWAfgEQCfkIrDBnAtgEchgt/3yn0B4D8BfIYxthkiRvHzwd5IMCYBeIaP/PtVsYgKbAPwpbHqMEoY1YhhqCKxcsfpJEGfMaEqJt8r7llkMIb/+/Ap+PwFxwDw9zzS93//KTOV60gHZXQRaTXXllYS9Bz06XlKScgGiBTIrtZIw3Y42tMFERCX59iyvw8x00CLNPh9gWpgWhVPrBaZWFnLUQaTssDKxSRoBW+prB+/ktBdNpxzX4NCMrTfeHQj0gXnkFanZEgLpQy87frrROS1KndTIEZRcFy4LlexHdpnj0xZnlafVEQS7HPVm7MUadsOR8F2VSaUZYvgd04eG+wdRggOlerOWmBMqGzb9WJJLQ0iEYPcY4A/njEQenIWjvvyo/j7pvIzzCmRQ1c3gH/R8Ni6ffjcfWsr/twQw49Kspue4pwzzvliPd2Vc/5Bzvkiuf1dnPM92jE3cc7ncs6P5pw/rG1/iHN+lHzvJm37Vs75Ms75PM75ZZzzQTtDS8UkyGgTaSyQvu75slNsYRD5+obB1ApLT0PV8e4TvXi7IdXIH647DT/+wInKTfT7TyzX9hHurKMni+sppST6AxEJEWRzTWkloabZaYbK1QLXgE4k5G4yYLvCLTKx2uv3tLmtDy0NSfVaz+DSIdxNJjIFR7leZjYJQ1TOgFNdgVe0J1ND5euewGp8j0YSwY62tNrP2w5+9MSWfpvX3fHUm/if33sppERWhVJKwnb9wWBJAhSopmsn9xOdJ+hu2tudQzJqikp2RwT/iWh0dxO5By03EE9wOd79o2dwy8MbAPi/OzrSJZREbUIoQ8vhaOsVz41IX6/MDi4A+sPand3oy9sVFfmREswrN5s/JhNidGFMT6bT4YtJmH6SoC/hybNEAtY7j58KDmB+czUqhR5kTsVKP7ZPnjkf+3py+PXzO1Xwc1p90hd8nlqnxRgkcdCZS8Uk+gMRD5FEMMWWcNHiKbjnhZ04YUaD2kZ2WpECxSSUu0nM427vK6Cx2iu429WVxRlHTVSvS2XXzGxMiZoO6W6itE1SEqUClQXbVe4VVT+Q9+aFAH4ffc7yZmzQdc1o9NKSe3IW6lJRPPLaXnztkQ040JfHF/9pIUrhK39cBwC48eJFAICurN/g02u6znTe727qyVmK+GjaX7rgoLEqhvZ0AXlLVK7XJiLoydmCJHpymFwnWqQUbAd7u3Pq958tOOCcoy9vY1ZTFXZ0ZORMdj9JvryjS70u1zQxnffHJLoylpx/IhIxqAU9kcQPVm3GzMYUZjZW4cAAmVM6SOXVJkp/B3X05S0ACaXU6NmF0wtHJ8ZNWw5fTMLwxyQ+/o65mFybwLkLJ6t93nX81JIunHLQ3T/JftxUQaMbhB54ph+9baXdTQPhAzJllXL0g4hHTNzzr29Rbi1AECXguZfIIHrKwkC2IIqvmqrjPtKaMSGlNQn0r+CPb6nDf1+4AIwxpKIRFBwX29szMA2myLJUxbW+SrYCJBEMsAJA3nJ8JKG/B3irYPo99OcGKb4W6fayiye2FRwnECvwFA31yOoMzO3I2w56srZXkOgIV9lk2UfLcjhatVYmWUt0MHZcjoaUVw/RmfE3X9RRrvZEDz7b0t1UnxI9waifF+C5m3Z0ZLB0ZgMaUtFBBa7pGdWWaFETBJF9IVj5PcQxkBBDg3GkJIqDxmQgLjmhBZec0HJI59cLusrFJPTPDgap1ftM//nQlAThspOm45ITphU1GewP37rseHzxogXKFUcujmotu4kMSGN1DE3VXvruzMaUIrGgknjg2tPUz8kYuah6MbnW62BLBm1vdw7/9+w2/Ps5R/sKx8g4e5k8/vRPcb2uqkLWr59Ax4pVK7B1fxoDgXOOvO1VlJcKXOetYiVBLTLmTKxCOu+oIPaUuiRe392DvC2URGN1DFsPpGE5YhztCTPq0ZO10JO1ldpqrIoha3nXMEG6m0S1f3HVtve6tKvGlyElCYyUhHA35RE1maqxAYQaKDjuoGISRGCVKQk/STiqh1TYqnw0Ylwqif62HSxmaK03+iMJyowqJwQMTUkQYdC2wcYkdAyGIADxbJo14psZaC0SNQyVg99UHfdVmM+ZWNWvu4mQlG65zW19mNaQVNf4jy3t2LSvF//f3S/itlVbsKmtz9fnKBi4ptd6TCVnOz71EYw5EDmQkdSf56oNbb6GioS87arYRl0yqoyYTk55xwse07XtlCQxd2K1WPHLnktTfErCQn0qBsbEMV2ZAhpSMeluctHamYHBhBrMFRwVxKY0btt1lbspGTWLSKFcP6zNbX0qYSFbcLBhby+OmlQj56MIl11zTcLnrq1NRFATjwwqJkHfg+C8Ex20IKF4EVWrU+A+GNAOMTowbkhCVxLBFLuDwb8sn40fXHGCej1T83eXCxADAysJjSPUHw1t04u0+nM3vfuEaThGBruHCjdefBwA4MSZYmiTriSaqmM+IzKnqdqb8d1Pe2nqNrutPSPmdshjfrBqM875zpNqdW8w+PpclY9J+JVDV8ZSCic4HpXIoUeRhPc8v//XTQC8oTuEPjmtEBBdhek69Mrngu0iLz+Lhjvt6MigJhFRnXLbZc+lKXIeec4SgWuahS6UhY26ZBSxiImC46K1M4vJtQnUJCLIWo66fnI32Y7nxppYEy8iiXJKYv2eHiyWg7jW7upGwXaxdGaDyl6j7sL686lJRFEVjyBnuRUHk4kk8v3EFehPojegJNJaLc1A9xPi8GPMkkSwxiFm6iQh/h/s6lrHl965EP+0eKp6PUNbSZeLSQADE5M/JhFwN2n79ackvv3eJXjk02/r93MGi+On12PbLRfhmMmyD5UcmwoAyaj/Wbc0JH1V2uWg12201CeLVpnkxsnbrlISTdUx2DJ/nz6fYhLdAXdTZ6aAKXUizhEkiR5VUyGO0TOqVAzBNHzb03lbtTyf0VilDNXGvb2YJRcJBdtVrq2ahChIa+3MYnpDShlez91ESkKQQm0iiphpqPTY+pR4XbBdtHZl0dKQQiomSIKIqVHGMSjoHI8Y4nOL3E3FSiJvO9jc1odFshZnzU4R6D5+er0YZiWVxKSaBKLaIqsmEVFp1ekK4wR0z6QK/r5pP5bf8lef4SfF3BeISVBVvh64JvU2GJdXiOHBmCWJoHnSV+705xIbRPB3IDTXxPH2oyfixx84sd/9lDooukKBUjEJ2vVgYxLDAX2KHhn3/7rwGJy9oFmR70DXqPemamlIlQ3mU7EdAEysScByXJU2O31CEpYjRp52ZizlOyclQX2dckF3U6DwTjeiRBJ5y/EVxvXmbDXSdcaEJFwuAvNbD6TxlrlNAIgkXBhMuH0KNkdXxssAs2TasGkwRZKZgo2+vI3apJhdQqtucjflbRe7OrNoaUgiERUZYXSN0ydQI0VBpA0p8Tk523+/tuuirTeH7zz2hlKkuzqzsF2OeROrwZhY7ZsGw2RtxG1br1AS+iKrJhFFdVxWvVcYJ6DEASKJWx/fhF1dWTy/zauGVyRRlMUmm0BqhNSVtbBudw+O/fKj+MXTb1Z0DSGGB2OWJILQq6SVu2kIDS1jDCs/tAznHzel3/30mMNA7xuKIygm4Rmy/ny7hwN6lTapo2veNhc/u+pktZ1y+Mv1wJqipfsePbmm7D2RkqhLRpGMGig4rgoGz24SacqOK4zxZG113p210JCKIRE1kLNdn4uCFAT9X5CdV4Ofq2dFpfM29vTkMKEqpgL4lGZ66pwJ6hiaThiNiFoSUgkUDN7fm0dTdUzV07T3eUHdqGkokqhLibG3mYKNPd1ZTGtIIhkzkLNEE0WDeUTruBxdMjMpajJfw0BAkOC1d7+M7/5lk2pvQvGVKdoEw+aaOEyDIWIa6Mvb6M5aqn08oTYZUY0k+3Mf6aDrIZVF6c4vy0l9nPOiGelEKPS7+cmT3qCjnOVgmxzqdMMf1pX93GzBwazP/wn3vdha0XWGGDzGDUnoi1QyBSOxGh/oI/0k4Y9J6P6m2EgrCe3zyz1HarMxp0y9id6x9ujJNWVdcXnbQYdsdR41DXRnLXz8rpfEuZuoSlu4WyjLLGc56MwU0CC7zWYLjm8lGmzhQefQiSJnOT6S6MvbaJfFg+RKIzcKqYKC7WjTCZlqp16bjMhgsFidT6yJqzgOJQDUJQPuJtnuJFNw4HJRU5OMigLE3V1i1KzKCHMESQqSMHzpsIQ32mTvK9lig9TI1LqkIn1KVoiaXi+y5pq4L8mjRrrFgOIZE+XQG1ASeal01sk5G3nbVa69YApswXbx0o4urG31CvksrU19f6DKdYozhRh6jF2SCNgb3fhSNfFgag2GCgMpCf3tgLfJF5MY6DzDDX3VX04BkOtn3sSBixLFXPBy7iahJGgextrWbhVjmCqDv13ZAmyXK3XSnbWQs1ypPkT7Dz2vnzJlgnMc9LbUOVm/QBAk4V0H4Bk/qh+hCX8J2YHXcqhQLqq+b3u6cphYHVe9tbbIlttTZVzmgFQW9amYbzEwVZJEViqJyXXeCt+S2U0NqRgipoHuTLEBpTgGGWMip8l1CUXQk6V7LlI0iMrfr4zuX59HUQ6ce21HiBxIPREhZAIZYfq5LcdF8OtuO/4UZ8ITG0VmGiVVEInFhzCTMYQfY/bJBs2W7h6hxeLIKImAOghA3+4pCeluGkXNMHUjUi6WQJilZX4Fcee/LMMvPnSyPE95JdGu+dsJ0ycki1JtaZysamyXiiERE4ZVL67LWsI49dd3KWe5yqUBCJLoSBcwodoz3hSzoPz/gu0iZztIyImHNBeiNhlVJLi7OyuVhKwTkZ1z9fsBpLLQjNuUuoTqgLvtQBpT67yMMHK3CWJhvoK+IKgOZXdXFg2pqCRoIglPSRCKYxIRdZ1Bkvjmoxvxu5f8rp2M1iuLOvWSAiPiLpW5VNDIIljnUnBc/4AkuS+N+n15R6c8rzhOf47vu/0fo3Lk7VjFmCWJIE34YxLi/0NJgT1Y0GeWM/g+d5N8+rSJl+3Ac/ihG5FyiozagDSVaVEOAG87aiLecXQzgOLfhzcXm5RE1PdZj376bcrwEkmQkiBXSn1KKIlM3lYty5uq48pw7e3OqToBK1AglrMcvL67W8WG0nkbB/ryaNLmfpMxro5HwJiXApuImIiahgq412qGtTcnqqvJ3bS5rQ8Rg2FKnUcSqZip3E2EyXUJFcfYLScEEkG3dmbRlbHQIKul+wNlPu3tzmFynb/zLlV96+pwUo0/JjEhFSurJH6wajM+c+8rvm26S4+UGqUBkxGnJo/6OfVhS8FqazFf3XOp0XmIeKmNO5E+Pet1u3vw7NaOwz7ydjxjDJOEwLT6JC5aNAVLZzUUvTcSSqJcfYR6v1RMQr7WiWWEvU0+11A5N9Hnzj0G/3b2UbhwUf/BfEKw5xUFh/tyNvb35X0B1FTMRCoWUVlWRBKTa4XRI9VQL91Nqzbux4+eEIHPOROrkCkIZWE5XLU1z5dQEq/v7sGJsqdVd9ZCT87GhKq4SgnVx97GTAN5cjdFDUQNQ82hrk36CW5ijedu6s3ZmNYg4gK0z5yJVTAMVpRVpDePnFyXUMb89ie3wna5dDf1/+XQBx8RqVJ2V0omGRDRxEwxVldPgY1oM9DLxSS6M5Zq6e1rl2KLoUykJOh5b5E1MbWJSJG7ST/Hty47Xl6vX0lQlhWRASkUcgcSefxh7W4AXo0OAGxvTw86Q2rl02/iI798YVDHjFeMeZKY3VSF295/oq/Yi6uYxAi4m4LB6AB0DmGBfXUdMdKup2iJ7KYg6lJRfOrs+f1WoOtIxkys/p+zcc7CSQCg0iw3t/XBcTlmN1Upo0mzuOl3SI3ommvjMJiXuVOfivnqVi5eMhVVsrGgXgkNkJLwVqx528GbB9I4enINElHDNwuD0qfJ3RSLCMOZt1wvcG0yZQxrE/4Vvu5uArzAN90PxXGCXQH0e5laoraEaiv6g63FJCgbjIiDSIjIqrk2DsZY0TljZdxNhH//7Rp87r612NzWqwLMjVUx5G0XHXImRdRkSrlt3d+HeMTA7KYqFByOe17Ygdd396jzBXs/BUmCzkPPq72vAM45Vm/r9G3/28b96hmQHbjyjudxwx/WDWrS3vV/WIfH17cNvOMRgDFPEqWMMdVHjUQa6cCB6+IUWNIS5Tp5jgR8gesB1NFg3HpN1fGi2RUb5JjQWU1euw+as0zXsV3GDqbWiVqCvZq7Sc+i+tY/L0EqFkG6YGOnNPpqHrfDfe6m7qylMqaqYhG1f6PmburL24iaYgRuPGLIwLWjAvG0QqcaCMLEar+fn66RVtGkbkjFnHesIE5dSejuJkJFSkIWI3akC6rrsCqMlP20SB1OKhGjADCgkqBqeb3XVVN1HHnLa20yd2K1yNxyObYeSGN2UxXiEROtnRn85/2v+gogiSRqEt70wq6s5cWGKG1WHnOgL4/fvtiKlc9sAyCURMF2sW5Pj6o9oWA5LRZ2dZVua98fwvblY5gkijuoeiDf/kgXpA2EYAqsThGjyd1kDkACpX4H/YHOF4+YiJoM62Ve/5ymKjXbuymgJLYeSKOpWrQfT0RNFSugmAQg6jVMg6kW5Ts7MmDM644bDFyT8ZhUm0AyZipVkIyZvuwmUqlUHZ2zHCSjpq9Ysy7pz1SaWCNW6KQm6L11cvVMEwkXTq3Fy188B9+QbhZdSUzRUlcJDVXRAb/XtusZaopJEGhQFRE7ZahVxSOojkfwdTmXfSAl4Q1KspWraGJNHDnZ9hwA5srU6HTBxr6eHKbWJxGNMKXYAO+7oJREwlMSPVlLtTbpC7RZOdCXx4tSRQDi+0pxEJqRTr9PWjRSE8VMwcZn7l3jS3Qoh8H0rxqvGN1WtAKUMk9edtMIW9oB4BXTSWgsMdKigtxNUZMNmgQGAhmoiMEQj5go2C7qU1HUp2Jq5UwrXNr3zQNpVX1MgehYxEAyaip3F7kqquSwo52dGaES4l76qt5plAr2mmvFgCRym0RNwxe4JkNfnYjgwVd2Y8v+NOJRQ11r1GSY2ZjyKwlpfElBBDvgnjjTi6E1VMWUcdSVxMQSCQF1yVgFJMFVYF8vaAS8+eHBGepR08BrN5yHf5Zz2QdKgaVsJNHF1iOJvOWq1Fty81Fn3AaZ8ksEc/dHTsHNl4gZHkElYTui+I4KNkkBUuPGA30FX9W57bg40CtIgfqatacLPnVOi4I/vLIbv3tpF259/I3yD1EiWLR4JGLsk0QJ+zWSxXSDQVEK7CjKbiIlMVAmzcGdmwjIUMaTKnTJdUEGhn6H3VlLzTygKuT6ZBSMMZWxQ2SRjEXEsKMO0VOJFgtCSQjDUpeMYnu7riQiylBFDObLVCJDf9VbZymjGY94aaXzmmsQNQ3fs6LeYnR/9P8ps0XldrmW2rrrrJSCa0hFB1z8UOGd2D/me49IKKLFJEqBSEKvYg8WIgJiZd+j3E0x5G2RMGAaTKVGU2qxPrwKEP3QSDn1ZG3EIoYK9tO0Phr/Syt6vRBPb9BYsF1VpHiUnDzZkc77phfulDM7KC5VTiHrjTaDc0qORIxuK1oBSiuJkSumGwxGd3YT8/0/lDAN8oszZRSpsnpHh/jDnyddFfrn09Ai+p/IgWonyH+eionOqm+2p9EyIakMtJ4CS0FWQJBEVcxUq/yIafjqJOj4K5bNwHuWirkk+3vzyuAtkCvXWMS7VjU1UBo9Mrp3f+QUbPjq+WWfTX/NIwFBbuWyzbyaClc9i6LAOAWuSbGV6WhMx+nFh3r/K5qtLTLCLMQiBmoTUbhcrNgnVscVEe7vzSMvDb6eRdWkjcXtyVpIRk11XbYj2qwElYSPJLQZIQXHm9dN8Z6erO0bx0qBcFoM6MkuOvTamu4Kqr7HO8YsSdCfY6lA8UgW0w0GRXUSo0dIIK6URGUk8b+XLMJvrjm1on09d5OhCH2WJAla+VGw2V8ZLEiBlMQxU4RxnqIa6YkVIimK/b15qSQ810lauo/IBRU1GRpSUV+GVtRkKjaStRxlyBhjuPqtswAAm9p6lcGi6yAjetq8JnUuFc+IeAFjXS0EQUayXCv4iKa+gqA2KZbD1bUE9yUSGlBJlGjLoY+dpboLGppUm/ASCDbs7cWMxpR6xuTmmZCKqe9VdTwiYj+aUkzKfliASJnNWo6qxaF24pTyur8vj968jc+eexSOm1aLgu1if5+/lsZyXLy2uwcGE6qFVCAFsMu1/dDb31fSGmS8Y0AryhibzhhbxRhbxxh7nTH2Kbl9AmPsMcbYJvl/g9zOGGPfY4xtZoytZYydqJ3rKrn/JsbYVdr2pYyxV+Ux32ODcIL3524aygZ/g0UlN0C3WWro0NuOmjj0FzUIxOUffJmpmEW44pQZOHVOY0X7eu4mht2SFI6aJFZ/333fCbhsaQumyoBrsPYA8IwVVQ9TmicNHvKnkSZ8rpOenIWaRFQZz+aaBBhjakASIIhJD0LHNaNOrowrls3AVllJTe3V3zK3EVe/dZZvDkkwcD0Qpk9I4pNnzcfPrz656L3HPyPaw1eVmbF+wnQxN8JxuTKIwYUSKQkyvnoLfB2lAtc0MVBHT85Cb070riLVtGFvL+Y1V6NKpjjTaNYJWtYYJSbQ76atN4dUzFQLiP97djs499x2eVs0ASRiJtVw1KQaNFbFlbupKmZ6cQ2XY8OeHsxuqkJ9KqoIjwLnHRoZ/Md9r2Dx9Y9iV1fWN0grdDdVpiRsAP/OOV8I4FQAn2CMLQTweQB/4ZzPB/AX+RoALgAwX/67BsCPAEEqAL4M4BQAywB8mf1cnpsAACAASURBVIhF7vNR7bjyepzAin5QUO6mEai4HgyCKsjlHAYDrn3HPJUaOVIg4+ZUyhKDgFISmgF7xzGiKnvpzAZ847LjvRG02j407OniJdNQHY/gMhlkpeArFVjpRrSxypvPXXBc4Ruv8moraCVdFVAS5AsH/KvxWMTAmzdfiH89Yy62Sp84zUqvTURx/buOVdPk9GPJgA4Exhg+c85RyqWmY16zIKiqQNfdafVJfP/yE/Cps48CIMiQYglBlytdx9uPasYjnz69aCIhwTAYIgbz9VfqzRcbzJ6siEnoxAuIOpBgjUtDVUwptDqZZED7dGYsOUrV/5ziUaGc8paYLR6c5T2jMaVSXvtytixq9BYFe3tyaGlIqcw0cT1iYXJAkkTOcnDv6lb05Gzs7Mhgb4+XfRUGrisgCc75Hs75S/LnXgDrAUwDsALAL+VuvwRwsfx5BYA7ucCzAOoZY1MAnAfgMc55B+e8E8BjAM6X79Vyzp/lwrrfqZ1r4Bvot05ilLubVBqv+J9zoSZGOh4BeAFU3cUwVKCYRNRg+H8ffyse+MTysv5hfbgUKYn5k2rw2g3nqdTWRmnQP7x8dtExjVofpoItRos2VEXVpEFKAdXVR8Q0UJeMqrYhQZcNKcDPX3AMauKRkllI+rkAIGZWVnBYCWiF7l0P8M7jp/qIXZFEmZiEYTClgMohFjHUef7zvrU47WurivZ5YM0uPPnGftQmIr7f4dxmjyQooNyQiqrnQESnx0z68nZRMDkm3Ws9ORuf/PXLAPwu0OkNgiQKkhhjEUO57Kht+0TZ5ZbiK+SWomp56lQLiO+IHuwOlQRQWreWAWNsFoATADwHYBLnfI98ay+ASfLnaQB2aoe1ym39bW8tsb3U518DoU5QN3UOkujfoI6ErR1MhpIXuFaha3A+MtcdBJHEMHCEWt1GTIYTBlBMFNAGPIMehGEwbL7pAmVgKLce8AdHLYejI1PAUZOqMV+uyul3oMckIoZI+51cm8C29kxZAvvYGXPxsTPm9nv99Lscynnr5dxN5MazXG92Q9DN1V88JIiotvr+3cu7Su5ja+2/dTKdkIqp3zMpvFjEUEpCkcQACzmR8WTi4df2qMBzc00cu7tziMnYUlxep+VwkWWmZbPt782juSaO9r48enM2ctpoWDqfHtwuyDnnNOM8XyYF+EhCxd9cxlg1gPsBfJpz3qO/JxXAsIddOee3c85P4pyflEgIQ1BqAtx7TxZuiIEyRUYarISS8G0YQdAqeniUBJP/D/z103thBd0sOiKmoVb4lCoLwJd2WbAdNd2tRQZ5aaWo95Wi/SnWkajQVVQK9KscylbW5Z4DBfkdx1Xxg6D7ZjDJHLRCD3ZoJejDpta2dvlcasmYqT6LmvHpsR5y7+kZYY98+vTiazANJKL+YD3NxKAgeExWWxcc11fjsr83D9vlSkkQaQAiPkIE+Ko2xyIvlcSUugTiUaPioUvjGRV9YxhjUQiCuJtz/ju5eZ90FUH+T41OdgGYrh3eIrf1t72lxPaKUMqefv78Y7Dhq+cPatU0VCg3trQUylVcjzxFeFk5wxCSUO6CSrnwyc+9A7/8l2UVn5/83YAw/no6Z2dGzItYNE1UPF96ovjq6UVswbba5ZREJaDvw1AqieB8dwJdty3dTabBBqyW7w9U+KbXI+iYqGVG3XTJIt9zSsa8OhIiGVNraEh/m7obrpT7S9TSmL6Z3qQo6ZlGTc3dZHpNFGkgkSAJkRZNrqZp9Uk1Ee/VXd3K/VhwxCwPGvhESuK1Xd1Y8MVH1DmPJFSS3cQA/BzAes75t7W3HgRAGUpXAXhA236lzHI6FUC3dEs9CuBcxliDDFifC+BR+V4PY+xU+VlXauca+AZKWBrDYCNCEMChuZtoWNIoEBKHtHoeCIONFc1oTOGMQ8j2IsPU1puHy0WBWXNtAlv/90K8W5KEvgomn/b8ScX1Dwd9DYdAEg9/6nSs+uzb1etUICYRbI1PJHGodUI5y8HvXtqFVRtLN7qjGoulMxtw+bIZvu9MKuqlt1KPpojBVIxEGfgBnm0sIpSE3h6Dkg3iEb+SsKSSYEyQI2XONdckVOCalMTU+gQ4F6m1m9r6sFS6PdN5B5v29WF+czXiEVORxP/9YzuyloNVG/ZX9OwGg+3tpUl4tKCSb+5yAB8EcCZjbI38dyGAWwCcwxjbBOBs+RoAHgKwFcBmAD8F8HEA4Jx3APgqgBfkv6/IbZD7/EweswXAwwNdVD/JTaMClRj6YOCaVu2DUSPDheEkWaUkhu0TgPOPnYwTZ4iU0FjEQGNVTLkVKHNJd2XpvnFaAX/k9Nn42qWL8JHT5xz0dQyFu2nBlFoVpAf6URJaIRq5XghvnVtZerIOqhf47uOlR4NS3QrFc4qVhCQJmZpsmkx956MqoF9BTEIz1gAwQTV/DJCEzb0iUINh8z7RE2xaQ1K4jmwXbTKziVyS1IH4OKksX9/dg7ztYlFLnVQS4trpu+IMcTHTa7u6ccY3nsCanV1Det6hxICBa875Uyj/93xWif05gE+UOdcdAO4osX01gOMGuhYfgn2PxiBUq3D5mo8mJXEILpaBMND0vqHAjz+41Pd6bnO1+kMsZWTjJdxN8YiJ954845Cu4/DGJDwlUbBdnwG+68OnHHTQcMaEFDbJEaw6KDZAgXT9HuMRA5CGndxNUcPrmkskEcy+CoKym3TQa/oexUwDtsuRtx3VvytmGujN26iJRzC1LiFdZw627E+jKmaqFGPq3zWrSZDGi9vFuvW4aXUyJuHKzxKfbQ1xIHuTnEs+mDbmhxujO0e0Aoz0LOggjp0qViSnzR/YPVKsJCRJDMuVDQ7D6W46FD/5wWJec7XXd6nEvekGNTqE/aqGIyaRKqPyDEOs1G99fBPufm6HT0kYhxCfKEUusYihYj86qRIYY0WBa9NgShHQ86hESeiq9o/XnabcaMHMsb68rc5H13TU5BrVjbdgu9jU1ot5k2rUd2CHdPVQvcgb+/qQiBqY3VhVpGAAqJ5YQwUq7BvNWVRjniRGGUfguGl1eOXL5+Jdx08dcF+P4Kjnjtw+CooADyVYOxD4CLjVqGEgUPredOIYjuc/lI0S+7s+/XMOlYy//M6FALw23Tqq4xFfASJQTL6mJC1Kx40YTNVdUJt1Mup6KxMdwt1UOmuK/nwo6aAnZ6v3yBU1v9kb7lRwXLwh4w30udTkkbLdAGBOUzUMOT+E3E1UVHcgPcQk0SXcX+W67Y4GjH2SGOkLKAE9u6Y/BN0u7ihq3jScSoJwOAme2osDpe8tPshgeqUIpjkPJUq5sPSGiIf6fVqxRJQr0eAlHamYWeT2KnU9ukE3DKbcNbT6NwyGxz9zBm6/cmnRsXS8r+JdS3GlRRa5mDrSBS8wLvehv8V4xADnIi12XnO1+vztHRk1j5yOmS9bxOjZTZ1SQQy1W4j6SOXt0ZtqO2ZJQq3BR5uUGASUAZGvR1NMIj6MgeuRmMCn18wMpCSGA0N9y898/kw8eO1p8tzeyXX1oLf5PhjQKN6+vO1LEQaEAQ260Eo9V7Wyl9dVUO1CtBYezdVF888JsUBDxIjppdEqkpALAMfliszUYCtKtdWuVR+Tu+1AumjmBqnOeMRUMQnqBnugr3IlsWFvD77wu7X9TrfbLUkiVBLDiNFgUA8WwXkSoyu7afi+GnSfhzOepBu5UiveoWybocObFTK0mFqfxMzGFBgDPnf+0Wq7bnwP1fCYmnoIFqaWJNqSSoKyjcR7V75lFibXJnDhoikVXUMswnznjRheRTV9fWo15U7Gn1QUfY/12MecpiqtsWBeddwlAqM2KyIjSqzwqTq7lOutHL70wOv49fM7fZlLqza0qUA+5zyMSRwOjAaDerAIzpMYTXUSlXYtHSvQV6OlVMNwKYmGlJdtM9RIRE28efNFuOQErxbVryQOjZpM7YsYXOmXCsSXipVEAkpiXnM1nv2vs1RTxoEQM03f7053NzGlJDySoPeoMWVCtWr3zjF9Qsp3/Ytl91wCnY/cTZxz1X9qMG4hcnURSby8oxMfWvkCvvHoRgCCeKiGZDAksfSrj+HHf9tS8f6HirFrCYbR13u4UL5OYuQxnG68kYi86IamVHrvcJHiV1Ych/+5aAFOnTNhWM4fRHQo3U3auZJR09dMM2J4EwGpWrkU6LmagyjsW7HES/oIBq4jJtNiEmJbXaofkgiMj6Vt+u97sayRIHhxDJHd1J211KySgYy57vqjn4kktsohSRv2iq5GFI8AKld9OctBe7qAWx7eUNH+Q4ExSxKkIEZBItBBw6uTkC6JUaQkhhMjcZ+6++xwKom6ZBQfOX3OYYud6cY42FZ70OcydCVh+irlTYNh+bwm/Pyqk/Bp2aK8FPTitkrx3fedoL4bUZP54mNR01AV8MGYBOBVcNtuwN0kSWKqjD/oSiKoavRgd95y0CpdQk3VcUUSjsuLYg1f+N2reOcPnlKvqUU6TdXbKIv7SOHReYH+yaetN4ctcnZJe4nsqh//bQuuf/D1sscDwOu7uw/a/ThmSYIwtt1N4n+lJFyqkxgd9/TD95+IRz/9tmE7/+G8z2TAZRHEeHGvDWWNh27XkzHTp1LI+J+1YFJR00Ca4w3ogevBXRcRQFBJRE2mzkV/N1WxiLpW+j2SAU8EAtc01VAniZqE35VWm5TFgbJKm1b8cyZWqYZ/H71zNY67/lHfcb9+fgde29WDta1COeyV1d0U69iwV5AEKQoKWjPWv5JYdtNfcNa3/gagOLvKdlzc8vAGrHxmW1nluLsri4u+9xRu+EP/RFIOY/4vYyyvuoMN/siFPFru6cJFU3B0mTGaYw3BeRFBDGc21+HEUBYqUg8kQCiJE2d6bd3LdfDdeOP5uPsjp6jXpCAGe116+w7dVagX6anJjgZTVd9RFbgW+9PigALORBI68aQCQXnd3WS7XI1fnTuxSs32/uuGNuQst+RQrrWt3bAdV8UxiADoPAf68nBdjl1dWSSiBhpSMRXroOmJ5RBUEi/t8ILiGyUJrdvdgw+vfEG1Q6HzPfdmBw4GIUmMIILZTSORGjoS4CNAhgO1GRkvSmKoB215JBHBjz6wVMULyrmP4hG/W8qb7T24X/Z9H3srPrR8FuIRo8iIU8aUfgkJuQ+RBLW4Jzci1U+cIPt56eon6Aqs0QLXAPCnV/egPhXF5NokHJf7OsEu+OIjyFmObxWft11s78io73nBdgUpaO6lnO1gd1cW0+qTqhocAK65czUWX/9ntV9QHbRrKbiZgq2m7AHAq3IuxmfuXYO/bGjDSzs6AYimhcDBT5kc1NCh0YTxWCfhZTeN3XuqBMdMEerk+EBWyXBioNkih9oxdbRgML7/SkAZTsmYiep4BCfNmoAH1uyuOH35YJXE8dPr1fcjSBJk6vRrqI5HsL83r36PwcD1Py2agkTEwNkLxGy0/tqkqBoLuc/LO7rwXxceo4z+Exu9TrAFR8yf0M+Xsxy8IVf1U+oSyNuiRXnBcXHUpGq8sa8PmYKDXV1ZTK1PYkdHRrmkVslzc87BGMPnfvuKd9+cq2l6ANDWk/epDkrTJbWxYW8vls9rUvscbCLDmF8+jeU/bUoZHI3ZTcOJ0+dPxJOfe0dFrUuGCgM12BsvxDzUM8nJyMcCqayVklGwmO5gkAyk3/ISfyfUtNFTEv4UWMNgOPfYyepvrhLlqLsgF7fUq+/QF373qm+/vO34VvR5y8HGfb1gDFg4pRYFx0Vrp3A1Ufv5bMGvJPKWi860rhIcOC7H79fsVttylhtQEo5vBnfedpDO26odOk3co0l8B/vdGLskMZ5SYEfhPInhxox+0iaHA+OFBAaCHoSttD1MfyCjqiqZ5XOsNKX1YAPXOoJKgv5OdCVB902fR0RSrii0lJL49j8fj0+eNb/onIDIgNJjI5cv8+anZQoO9mlzsXO2iy3702hpSKoxqBS0PlqSxK6uLA70FTCtPqn6Sj33Zrs6R7pgK2I5dmqt2kZqQXyOg56chaisQs9ZrsqCArzAeI+sFj/YbLexSxISo60L7GBQNJnuCFESIYYPi1tkF+J5TUOSmaaUA9UmDFpJ0PEH/60OkgS5g6q04UuekvB/Trm5KKVI4t0ntuAz53jpvLMa/fPV9TTpGy9ehNtlO/psoVhJ7OvOYUpdUs26+NVzO3DUpGocN00Y/P/+f68iajKctWCSrMdw8I8tGknkHbyxTxh8crtlC47PvZQrOOjJWqhNRFV1+CZ5TEtDUgXZ6ZgjTkmwwP9jEcGYBP0SR0MX2BBjE2ceI3zuZy9oVjO6DwVKSQS+k5XGGIhcDiXrKljtvXRGAz551nx8/T3Hq23UmiPY1LAsSVTgbpqlDXqqikd8rUhMg6nnmyk42Nsj4iHNNXHkLBdtvTk0y9naWcvB67t7cP6xk9W9bNmfxpVvmYWFU2vV1LwXtnWq86fzNjbLGR5U7JcpeMoBEEqiN2ejNhlFPGIiZ7nY1NaHqMlwzOQa5Ar+DrbpfOUtRXSM2cA1YSy7EcwAS4ymeRIhxibeMrcRf/rkacqtcaig72JEVTLL2RAV/t0FYxkHg6CSMAzmW/EDnmuoN9BbqZy7qRK7ERxOFYxr0XVlCjbaenJorkkgYjLkbAdtvXk01yRgMO+aapNR3728+0TRZTcWMdDVa2FzWy8Wt9RhbWs3+vI2dndlUZeMqmK/dMFGb85Gc00Cu7qyyBZEumxtIgLLcZG3HGzqyWF2UxVqElFkrT75TISSyNuu6hs1GIx5khjLKJ5xLd8Yw8Q3lrFgSq2aPzCWQYOvhgL0lYyorCH/64EQCXRlPRgMlJkGADXxMiTRT+rzZ889Cm+ZW3qOhTpvIlJyPKu4LvGZ2YKDvT05TKqNI5130JEuIFNw0FwbV/EAQPSE0kmCjH88YmD9HtGqY+nMBqxt7UamYGNPdw5T6hLqmKx0LzXXxgVJWNLdlIwiXXCQt11s3NeLJdPrkYiaqi9Uj/ZMqKvtYDDmSWIs29PimESoJIYT37rs+LKjPwHg4U+dfhivZmyAvpNUya2UxCCzm4JV2YNBuUl8Oha1CL899ZGqT0XRlbH6dd1ee+b8su8RXvjvs9XP8UBH2SqlJERM4ujJNdjVmVVFc8L15K3caxIRX6ZWQ0rMWtfjIyfNnIBfPL0NfXlxzkm1CeWiSudt9ORsVeCasxz05GxMqUuiI/3/t3fv0XrV9Z3H35/nXHIPSbjEkIRLYxADAkompAolYMVgdSWO1IoXokXRqdRqpx2ZTmfRKTrTdtWZNVZrB8cIzlIorXWgSoU0yxm1U5WAysVLSbmYpEgCCSgJEEi+88f+7efsc3IO5zz3vZ/zea111nme377kt/Ocvb/P736Qx/c/y859T/Nrq5ez78BzPHPwEBHBD/7lyfr5n2li3YrKBomyTF3RCqW/jSPHSfQmP/3uTWcvm3wnGyUv3eYlgryHzFR7K41d36EZUxkg+JpVi/nKB85l1ZKsYfhvrzy3PgK5FeNNDJk/1GcVqpt++uQznLfyWB576iAPp66nx82bWZ+aA7JBesWAl/+f5KPC588c5MzlWSlw/7NZSeK04+czOzXQ5yWHvATy9MGs1DJ/1hAzhwa4b1dWGjnlRfO4e+cTPP3cIf7xgcf55z37OW/lMXzj/seaqm6a9H9f0mZJuyXdW0j7A0m7JH0v/byusO3fS9ou6ceSXltIX5/Stku6qpB+sqRvp/S/lDQ8pZz3wYO0zOtJmEGhJDFmdtWpPvTzh2y7B/mN57Tjj6rfS8sXzeaXVy1u6/lnjJkscHigxkBNPPDYfvYfPMSKY+fUV8CDrFRTbCDPShJHlopek/J5/IJZ9dHeTxx4jseeepYXFaqbHn/qIM8fDo5L61088Nh+9u4/yEuXzGPGYI2fp4bplyyex6yhbEqR76SpOH4lrd/xTBPVTVP5OnAdsH6c9P8WEWeln1sBJK0C3gKclo75c0kDkgaATwIXA6uAS9O+AH+czvViYB9wecNXUVEj4yQyLklY2eRfXIbGlCSmGiTytoIqdzDJ5Q/84cLcUbOHBrh7Z1ZyWLl4Xj0ozh4eqA+Uy82bOTjuoM7XrFrMv1m3gj+55Ix6FdaONEbimLkz6tVNn/1/DwKwaM4MBmvihu/8BIAzly0YNSX68kWz6+/vfHgfJyyaXV9I6elOlCQi4uvAVGeG2gDcGBHPRsSDwHZgTfrZHhEPRMRB4EZgg7K/nAuBv07HXw9sbPAaKsvjJKzs8pJEXr00OKaKZDL5N+Myr7w2VXkjfj4dOWRVTnlX1ZXHza0/nFceN5daTaPaG+bOHBw3WEriw+tP5YxlCxhMa3rno69nDNaYNyMLLjv2ZoPj5s0cHDUw7tRUkoBsfe6Bmuollrse3seqJfPr+epIddMLuFLS3ak6Kp8ecimwo7DPzpQ2UfrRwBMR8fyY9HFJukLSNknbDuzf30LWy2GkB2yqbjrskoSVS4xpk3j72hP5jXUreN/5K6Z0fN41Ne+z36wLTz2Ot689oaVztGrx/Ozb+O++9tR6Wl4VtHj+DI6eO6PwsM4al4tBIl/xbtnCWbz3/F+Y8N+ZN3OoPv3G8GCNWk1cs+H0+vbiIMI/ueQMZgyOrN6Xr8+dB/H9Bw+x6vj59a7A3ewC+yngGrLgeg3wMeDXmzzXlEXEtcC1ACe+5GWVnzK1dsQ4ifyto4SVw+ExbRIzhwb4d+tPfaFDRpmbgsSB55obyJXb/M5/1dLx7TB7eJCH/uhXRqXl/y8Xn57X+WcP4VMWZw/r4trpeQD55ocvfMF/Z8HsIfakacbrVVzFlfUKXXHXnLRo1H75AL9ig/uqJfPr3Xc71SZxhIh4NCIORcRh4NNk1UkAu4DlhV2XpbSJ0h8HFkgaHJM+LYytbjrs+iYrmXrvpiYbnvPqpqdbLEmU1ZvOXsaZy47ibedkpZwdaTrw8UoSU22XWTBrqD5JXx6Eim0ZxXPWR32n4LQk9XwqVgdmJYnsfSNrdOeaChKSlhTevhHIez7dArxF0gxJJwMrge8AdwArU0+mYbLG7Vsiq/D8GnBJOn4TcHMzeaqi/L6r1Xs3eZyElUswuiTRqHzUcjPfYKvgfeev4OYrz60HhXyMxCljgkQjVcgLZg/xVOqpVO9JVQgMxUF9+cP/iQNZ9dR4JYklR83sbHWTpBuAdcAxknYCVwPrJJ1FVt30EPBegIi4T9JNwA+A54H3R8ShdJ4rgduAAWBzRORr6X0YuFHSR4DvAp+ZUs774Ek6ssZ1ZrqsJ2HVMXacRKPy9acPHGytuqkq/uzSl/PZf3iwvpZ23ivs5MJkgZM5atbIKICRkkSh2mqcqUb2pSBxXF6SSG0lwwM1JNVLFs0E60mDRERcOk7yhA/yiPgo8NFx0m8Fbh0n/QFGqqumrJ8eo/XlS9Pn10/XZhVXr25qriRRr25q4htsFb3qxcfwqhePTPWxb382LcfJxzQSJEameB9OPamKgWHGYI2b3vuLoyYzzKfbWJRGcee1FBeceixAS72bKjviup/kDdV5d8M2rmVv1pKRhuvmvrrkDdf9Wt00mVeuOJpTFs9tqLF/weyRIDE0ZmwGZKWKNScvGnXMJ976Cv5q2w5OWJRNS/KypUdxzcbTeePLs86iMztZkrDOG9tw7d5NVhb1sQFNtknkg8OWLpjVphxVy8I5w9z+ofMbOqYYJPK2iLElibFe8qJ5/P7rV9XfS+Ida0+svx+oiaE0Q22jKh8kovIdYUfUu8A6RlhJ5F9cmm2TkMTmd67mpWlOJZtcsbpp3JLEBNOfT2bm4ICrm6rqiC6wZiWR/0m2MotrvhCSTU1xHYs8OBTX257KgknjmTE00L1xEmXSD9+6R9ok0vt+uCjrK92YoM8yxTEO9eqmQhXTVGbFHc/MoRrPdnlajlLohy/fR7ZJmJVLKyUJa0xxptihcUZcN2ugpqZqK/zJl8DIOIn03lHCSqbZNglrXHFN77xX2XiN1Y2qSRxu4ku1g0QJ6IgR174hrVyaHSdhjSsucVpcu6JVEhxySaKa8pAQXk/CSqrZcRLWuFHVTbWRtStaVZPqz5iGjmv5X7aW1dskPOLaSqrZxlJrXLEk8UJrdDdqQKo/YxrhT74EjqhucpSwknFJonuKU4G3k9RcN3sHiR44es74y3iPNCr5hrRyGXKbRNe0s/Qw6rxNNlx7MF0P/P1vn8/PnnluVJrkNgkrr049uKx7ajXcJlEVC+cMc+KYqYPFSM+DmqOElcS7XnVSr7NgbZKVJBoPEi5JlERN4tBhD6azcrn6Dadx9RtO63U2DPjEW18+al6nRsnVTdWWVTeNvDYzK3r9Gce3dHzNDdfVJuTeTWbWMdk4icaPc0miLOQR12aWeeWKo9m7/2Bbz9lsScJBoiREoQusY4TZtPaF96xt+znVZMO1q5tKYlQX2B7nxcz6T1aSaOK4yXaQtFnSbkn3FtIWSdoi6f70e2FKl6SPS9ou6W5Jrygcsyntf7+kTYX0syXdk475uKbpYgpZm0R6PT3/C8ysgzo5d9N1wPoxaVcBWyNiJbA1vQe4GFiZfq4APgVZUAGuBs4B1gBX54El7fOewnFj/61pQfJ6EmbWOR2bKjwivg7sHZO8Abg+vb4e2FhI/1xkvgUskLQEeC2wJSL2RsQ+YAuwPm2bHxHfiizEfa5wrmml2CbhgoSZtVu3525aHBGPpNc/BfJFbJcCOwr77UxpL5S+c5z0cUm6QtI2Sdv2P7W/yayXkwpFQfduMrN269miQ6kE0JVFRCPi2ohYHRGr58ydM/kBFZKVJDxOwsw6o6buzt30aKoqIv3endJ3AcsL+y1LaS+Uvmyc9OlHXk/CzDqn2bmbmg0StwB5D6VNwM2F9MtSL6e1wJOpWuo24CJJC1OD9UXAbWnbzyStTb2aLiuca1opliQcGMLjEAAADQJJREFUJcys3dTkokOTDqaTdAOwDjhG0k6yXkp/BNwk6XLgYeDNafdbgdcB24EDwLsAImKvpGuAO9J+fxgReWP4b5D1oJoF/F36mXaKA108C6yZtdtArUMjriPi0gk2vXqcfQN4/wTn2QxsHid9G3D6ZPnodyoMdHGIMLN263Z1k7XZ6IZrhwkza6+e9W6y9lBhhkbHCDNrN69xXXGjShK9zYqZ9aFmpwp3kCgJFVemc5QwszabtosONTM4pIyKK9O5LGFm7eaG64rziGsz66Rmx0lUPkj0S08gzwJrZp3U7Wk5SqNvqpu8noSZdZC7wFacV6Yzs06qNTni2kGiJLyehJl1kqZbSaLfnqPFuZu8noSZtdu0bZPoJ4c9TsLMOsRdYCtu1AR/DhJm1mZuuK640V1gHSXMrL2m3dxNs4ezWc5/6ZRje5yT9si6wLq6ycw6o9m5myZdT6KsZg0PcNdH1jNjcKDXWWkLVzeZWSdNy7mb+iVAQNZbK1zdZGYd4obriiv2YXZJwszarVZzw3WleT0JM+skj5OousJU4S5JmFm79aQLrKSHJN0j6XuStqW0RZK2SLo//V6Y0iXp45K2S7pb0isK59mU9r9f0qZW8lRVtVGRwVHCzNqruLBZI9pRkrggIs6KiNXp/VXA1ohYCWxN7wEuBlamnyuAT0EWVICrgXOANcDVeWCZTkaFCMcIM2uzWpPPlU5UN20Ark+vrwc2FtI/F5lvAQskLQFeC2yJiL0RsQ/YAqzvQL5KrRgYHCPMrN1qTX77bDVIBHC7pDslXZHSFkfEI+n1T4HF6fVSYEfh2J0pbaL0I0i6QtI2Sdv27NnTYtbLpdjt1etJmFm7NVuSaHUw3bkRsUvSccAWST8qboyIkNS2VYEi4lrgWoDVq1f3x2pDiUsSZtZJzX75bKkkERG70u/dwJfI2hQeTdVIpN+70+67gOWFw5eltInSpy0XJMys3bpe3SRpjqR5+WvgIuBe4BYg76G0Cbg5vb4FuCz1cloLPJmqpW4DLpK0MDVYX5TSppVilG/2wzQzm0gvqpsWA19KD7dB4AsR8VVJdwA3SboceBh4c9r/VuB1wHbgAPAugIjYK+ka4I603x9GxN4W8lVJDgtm1knNfvlsOkhExAPAmeOkPw68epz0AN4/wbk2A5ubzUs/GNUm4YhhZm3W7HPFI65LYnSQcJQws/bqVRdYa5NRXWB7mA8z609lGkxnTXB1k5l1Uq3JKOEgURKjZ25ylDCz9urJOAlrI2m8l2ZmbeHqporzHLBm1kluuK44zxRuZp3kkkTFuU3CzDrJJYmKk9skzKyDHCQqrlgUdIwws3arNfm0d5AoiWIVkyf4M7N2c0mi6jyYzsw6yOMkKs4N12bWSe7dVHHuAmtmneTqpoobvcZ1DzNiZn3JJYmK8xrXZtZJbpOoOK8nYWad5OqmivN6EmbWSa5uqjivJ2FmneSSRB9xF1gza7fKr3Etab2kH0vaLumqXuen2zx3k5l1UqVLEpIGgE8CFwOrgEslreptrrrLccHMOqnSQQJYA2yPiAci4iBwI7Chx3nqKrdJmFknVb3heimwo/B+Z0obRdIVkrZJ2rZnz56uZa4bzlh6FABnn7iQ4YGyfCxm1i+a7VqviGhzVprIhHQJsD4i3p3evwM4JyKunOiY1atXx7Zt27qVxa44+PxhhgcdIMys/SKCCBgYqN0ZEaunetxgJzPVgF3A8sL7ZSltWnGAMLNOkdRUVXZZnkp3ACslnSxpGHgLcEuP82RmNu2VoiQREc9LuhK4DRgANkfEfT3OlpnZtFeKIAEQEbcCt/Y6H2ZmNqIs1U1mZlZCDhJmZjYhBwkzM5uQg4SZmU2oFIPpmiHpaaBdPaCOAp4swTlyJwA/acN52pmnMl4flPMa+/36oHzXWMZrK+tnd1pEzJry3tkovOr9AHvaeK5ry3COdl9bm/NUuusr6zX2+/WV8RrLeG398tlVubrpiTae629Lco5cu66tnXkq4/VBOa+x368PyneNZby2vvjsqlzdtC0amH+kSvr52sDX1w/6+Rr7+dqg8eurckni2l5noIP6+drA19cP+vka+/naoMHrq2xJwszMOq/KJQkzM+swBwkzM5tQ6YOEpKd6nYdOkLRRUkg6tdd56aTJPj9J/0dS5RoJJS2TdLOk+yX9s6T/nqa5n2j/D0qa3c08tqpf7z3w/VfYPun9V/og0ccuBb6Zfk+ZpIHOZMemStk6kH8D/O+IWAmcAswFPvoCh30QqFSQ6HO+/6aoEkFC0lxJWyXdJekeSRtS+kmSfijp05Luk3S7pKmPJOwRSXOBc4HLyRZYQtI6SV+X9BVJP5b0F5JqadtTkj4m6fvAL/Yu581J1/blwvtPSHpnD7PUqguBZyLiswARcQj4EPDrkuZI+lNJ90q6W9JvSvoAcDzwNUlf62G+G9Zv9x74/mv0/qtEkACeAd4YEa8ALgA+ppFVvVcCn4yI08gGibypR3lsxAbgqxHxT8Djks5O6WuA3wRWASuAf53S5wDfjogzI+KbXc+tjXUacGcxISJ+RjaVw7uBk4CzIuIM4PMR8XHgX4ALIuKCLue1Vf1274Hvv4ZUJUgI+M+S7gb+HlgKLE7bHoyI76XXd5LdoGV3KXBjen0jI0Xe70TEA+mb6Q1k33YADgFf7G4WrUnrgP8REc8DRMTe3manZf1274Hvv4aUZmW6SbwNOBY4OyKek/QQMDNte7aw3yGg1EVeSYvIqiteJinIlmsN4Cvpd1H+/pn0h1tVzzP6C8nMiXasiB8AlxQTJM0nmxjuoV5kqIP65t4D339JQ/dfVUoSRwG70x/pBcCJvc5QCy4B/ldEnBgRJ0XEcuBB4DxgjaSTU13or5E1rPWDh4FVkmZIWgC8utcZatFWYLaky6DemPkx4DqyddrfK2kwbVuUjvk5MK/7WW1ZP9174Puv4fuv1EEi3WjPAp8HVku6B7gM+FFPM9aaS4EvjUn7Ykq/A/gE8EOyP9yx+1VK/vlFxA7gJuDe9Pu7Pc1YiyKbpuCNwK9Kuh/4J7K6+98D/idZ28TdqaHzremwa4GvVqXhuk/vPfD91/D9V+ppOSSdCXw6Itb0Oi+dJmkd8DsR8fpe56VdptPn12+m22fn+29ipS1JSHofWePR7/c6L9Y4f37V5c+u+tr5GZa6JGFmZr1V2pKEmZn1XmmChKTlkr4m6QdpBOdvpfRFkrYomyNni6SFKf1USf8o6VlJvzPmXB9K57hX0g2Sqt7l0qyj2nz//Va69+6T9MFeXI+1T2mCBFlf3n8bEauAtcD7Ja0CrgK2pjlytqb3AHuBDwB/WjyJpKUpfXVEnE7WD/ot3bkEs8pq1/13OvAestHLZwKvl/Ti7lyCdUJpgkREPBIRd6XXPyfrhraUbAj99Wm364GNaZ/dEXEH8Nw4pxsEZqUuYLPJpkQwswm08f57KdkUFgfSqPP/y8j0FlZBpQkSRZJOAl4OfBtYHBGPpE0/ZWRKgHFFxC6ybzc/AR4BnoyI2zuWWbM+08r9R9YX/zxJRyubGv11wPIOZdW6oHRBIs3Q+EXgg2nStLo0iOkFu2OlOtMNwMlkM2/OkfT2DmXXrK+0ev9FxA+BPwZuB74KfI9syg6rqFIFCUlDZH+gn4+Iv0nJj0pakrYvAXZPcppfJpt4bE9EPEc27/8rO5Vns37RpvuPiPhMRJwdEb8E7CMbkW4VVZogkaYf/gzww4j4r4VNtwCb0utNwM2TnOonwFpJs9M5X01Wv2pmE2jj/Yek49LvE8jaI77Q3txaN5VmMJ2kc4FvAPcAh1Py75HVi95ENsPmw8CbI2KvpBcB24D5af+ngFUR8TNJ/4lsgq7nyeYpeXdEFGesNLOCNt9/3wCOJmvU/u2I2NrVi7G2Kk2QMDOz8ilNdZOZmZWPg4SZmU3IQcLMzCbkIGFmZhNykDAzswk5SJg1QdIfjJ39dMz2jWmCPLNKc5Aw64yNgIOEVZ7HSZhNkaT/QDbqeDewA7gTeBK4AhgGtgPvAM4Cvpy2PQm8KZ3ik8CxwAHgPRHxo27m36wZDhJmUyDpbOA64ByyqejvAv4C+GxEPJ72+QjwaET8maTrgC9HxF+nbVuB90XE/ZLOAf5LRFzY/Ssxa8xgrzNgVhHnAV+KiAMAkm5J6aen4LAAmAvcNvbANLPqK4G/yqZIAmBGx3Ns1gYOEmatuQ7YGBHfl/ROYN04+9SAJyLirC7my6wt3HBtNjVfBzZKmiVpHvCGlD4PeCRNs/22wv4/T9tI6zI8KOlXIZtxVdKZ3cu6WfMcJMymIC3t+ZfA94G/A+5Im/4j2Uyp/wAUG6JvBH5X0nclrSALIJdL+j5wH9nCWGal54ZrMzObkEsSZmY2IQcJMzObkIOEmZlNyEHCzMwm5CBhZmYTcpAwM7MJOUiYmdmE/j8PugAfWjB5/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Next let's plot this per type:\n",
    "typeperday_df.loc['2018-01-01':'2019-07-30'].pivot_table(index='date', \n",
    "                                                         columns='type', \n",
    "                                                         values='cnt', \n",
    "                                                         aggfunc='sum').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fixing our Time Series data\n",
    "\n",
    "Some details of what caused this drop:\n",
    "#### On August 14, 2018, Mayor de Blasio signed Local Law 149 of 2018, creating a new license category for TLC-licensed FHV businesses that currently dispatch or plan to dispatch more than 10,000 FHV trips in New York City per day under a single brand, trade, or operating name, referred to as High-Volume For-Hire Services (HVFHS). This law went into effect on Feb 1, 2019\n",
    "\n",
    "Let's bring the other license type and see how it affects the time series charts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "create_crawler_resp = glue_client.create_crawler(\n",
    "    Name=crawler_name + '_fhvhv',\n",
    "    Role='GlueRole',\n",
    "    DatabaseName=database_name,\n",
    "    Description='Crawler to discover the base tables for the workshop',\n",
    "    Targets={\n",
    "        'S3Targets': [\n",
    "            {\n",
    "                'Path': 's3://serverless-analytics/reinvent-2019_moredata/taxi_data/fhvhv/',\n",
    "            },\n",
    "        ]\n",
    "    }\n",
    ")\n",
    "response = glue_client.start_crawler(\n",
    "    Name=crawler_name + '_fhvhv'\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wait to discover the fhvhv dataset..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING\n",
      "RUNNING\n",
      "STOPPING\n",
      "STOPPING\n",
      "STOPPING\n",
      "STOPPING\n",
      "finished running READY\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    " \n",
    "response = glue_client.get_crawler(\n",
    "    Name=crawler_name + '_fhvhv'\n",
    ")\n",
    "while (response['Crawler']['State'] == 'RUNNING') | (response['Crawler']['State'] == 'STOPPING'):\n",
    "    print(response['Crawler']['State'])\n",
    "    # Wait for 40 seconds\n",
    "    time.sleep(40)\n",
    "    \n",
    "    response = glue_client.get_crawler(\n",
    "        Name=crawler_name + '_fhvhv'\n",
    "    )\n",
    "\n",
    "print('finished running', response['Crawler']['State'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             type     cnt\n",
      "date                     \n",
      "2019-05-12  fhvhv  857727\n",
      "2019-03-22  fhvhv  846827\n",
      "2019-03-08  fhvhv  853746\n",
      "2019-06-25  fhvhv  651649\n",
      "2019-03-01  fhvhv  837591\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa33e065cf8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query = 'select \\'fhvhv\\' as type, date_trunc(\\'day\\', cast(pickup_datetime as timestamp)) date, count(*) cnt from \"' + database_name + '\".\"fhvhv\" group by date_trunc(\\'day\\',  cast(pickup_datetime as timestamp)) '\n",
    "typeperday_fhvhv_df = pd.read_sql(query, conn)\n",
    "typeperday_fhvhv_df = typeperday_fhvhv_df.set_index('date', drop=True)\n",
    "print(typeperday_fhvhv_df.head())\n",
    "typeperday_fhvhv_df.plot(y='cnt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa33dfa0cf8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.concat([typeperday_fhvhv_df, typeperday_df], sort=False).loc['2018-01-01':'2019-07-30'].pivot_table(index='date', \n",
    "                                                         columns='type', \n",
    "                                                         values='cnt', \n",
    "                                                         aggfunc='sum').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### That looks better -- let's start looking at performing EDA now. Please open the other notebook file in your SageMaker notebook instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_python3",
   "language": "python",
   "name": "conda_python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
